Cloud-native features are helping public sector teams innovate faster than ever. Ideas discussed in a morning meeting can be a working proof of concept later that day. Managed services can remove administrative burden and reduce the steps needed to design and provision cloud infrastructure. Security can be built-in from the beginning with Identity and Access Management (IAM), Virtual Private Cloud Service Controls (VPC-SC), and Data Loss Prevention (DLP). Short-lived services and infrastructure-as-code allow rapid and cost-effective prototyping. These technologies can be used to architect a solution that follows the principle of least privilege and helps you secure your data.

So the question becomes: given the complex problems agencies face, where do you start? Google Public Sector now offers “Getting Started” and “Scaling” service offerings for CCAI, DocAI, and BigQuery to help you jumpstart your AI journey, based on where you are.

Complex problems, simpler AI-based solutions

Solving more challenging problems with cloud-native technology doesn’t have to be overwhelming. You can approach them the same way you might solve a puzzle: start with one piece that follows another until the larger picture takes shape. Though you can simplify the steps, solving these problems still requires powerful tools. Google Cloud’s AI/ML capabilities may be the answer for your team.

Google Public Sector is making it easier to get started with advanced technologies, beginning with artificial intelligence and machine learning (AI/ML) workloads for government organizations, and it’s something you can do now, one piece at a time.

Automating your FAQs with CCAI

Does your agency require a team to answer commonly asked questions? What if you could train an agent to answer questions immediately and operate 24/7? Contact Center AI (CCAI) can do this and more. Already using CCAI and need the agent to level up to address complex interactive dialogs? Getting Started with CCAI and Scaling with CCAI are new Google service offerings specifically designed to help public sector organizations tackle situations like these.

Automate data entry with DocAI

How many hours does your team spend manually reviewing or entering data from standardized forms? What if you could automatically pull data right from the page? Google Document AI (DocAI) specializes in exactly this—even if the form has handwritten text. Getting Started with DocAI and Scaling with DocAI are new service offerings that help you remove this burden from your team. DocAI automates data entry and makes that data available to other teams while prioritizing both security and ease of use.

Making data and insights accessible with BigQuery

Then there’s all your existing data. You may have years of it stored in many places, and you may not have a way to make use of it when you need it. BigQuery is Google’s enterprise data warehouse. It was designed for data analytics—looking back at historical data to make conclusions about it. But BigQuery’s analytics don’t stop there. It can also look forward in time to make predictions, often using the same datasets. Getting Started with BigQuery and Scaling with BigQuery are new service offerings that help you take your first steps toward AI/ML capabilities by starting with a single table or pipeline that can help make sense of all your data.

The best help is the kind that meets you where you are and gets you where you want to be. Google Public Sector’s new service offerings do just that: help you work through complex problems by meeting you wherever your starting line is, whether you’re ready to start or ready to scale. Let us know if you would like us to contact you about the services mentioned in this article. Let’s solve your highest impact problems together, one puzzle piece at a time.

In-memory data stores are a fundamental infrastructure for building scalable, high-performance applications. Whether it is building a highly responsive ecommerce website, creating multiplayer games with thousands of users, or doing real-time analysis on data pipelines with millions of events, an in-memory store helps provide low latency and scale for millions of transactions. Redis is a popular in-memory data store for use cases like session stores, gaming leaderboards, stream analytics, API rate limiting, threat detection, and more. Another in-memory data store, open source Memcached, continues to be a very popular choice as a caching layer for databases and is used for its speed and simplicity.

We’re announcing Memorystore for Memcached in beta, a fully managed, highly scalable service that’s compatible with the open source Memcached protocol. We launched Memorystore for Redis in 2018 to let you use the power of open source Redis easily without the burden of management. This announcement brings even more flexibility and choice for your caching layer. 

Highlights of Memorystore for Memcached

Memcached offers a simple but powerful in-memory key value store and is popular as a front-end cache for databases. Using Memcached as a front-end store not only provides an in-memory caching layer for faster query processing, but it can also help save costs by reducing the load on your back-end databases.

Using Memorystore for Memcached provides several important benefits:

• Memorystore for Memcached is fully open source protocol compatible. If you are migrating applications using self-deployed Memcached or other cloud providers, you can simply migrate your application with zero code changes.
• Memorystore for Memcached is fully managed. All the common tasks that you spend time on, like deployment, scaling, managing node configuration on the client, setting up monitoring, and patching, are all taken care of. You can focus on building your applications.
• Right-sizing a cache is a common challenge with distributed caches. The scaling feature of Memorystore for Memcached, along with detailed open source Memcached monitoring metrics, allows you to scale your instance up and down easily to optimize for your cache-hit ratio and price. With Memorystore for Memcached, you can scale your cluster up to 5 TB per instance.
• Auto-discovery protocol lets clients adapt to changes programmatically, making it easy to deal with changes to the number of nodes during scaling. This drastically reduces manageability overhead and code complexity.
• You can monitor your Memorystore for Memcached instances with built-in dashboards in the Cloud Console and rich metrics in Cloud Monitoring.

Memorystore for Memcached can be accessed from applications running on Compute Engine, Google Kubernetes Engine (GKE), App Engine Flex, App Engine Standard, and Cloud Functions.

The beta launch is available in major regions across the U.S., Asia, and Europe, and will be available globally soon.

Getting started with Memorystore for Memcached

To get started with Memorystore for Memcached, check out the quick start guide. Sign up for a $300 credit to try Memorystore and the rest of Google Cloud. You can start with the smallest instance and when you’re ready, you can easily scale up to serve performance-intensive applications. Enjoy your exploration of Google Cloud and Memorystore for Memcached.

Global life science supply chains are lengthy and complex with many moving parts. One small disruption can create serious delays and affect your ability to deliver therapeutics for patients.

Supply chain disruptors

Over the last few years, healthcare organizations have encountered a range of obstacles, from both internal and external factors, that have resulted in supply networks failing to get drugs and medical devices to where they need to be on time. These obstacles include:

• Labor and supply shortages
• Rising material costs
• Raw material constraints
• Geo-political events
• Unpredictable weather

How do you overcome supply chain disruptors that are out of your control?

The intelligent healthcare supply chain

While many organizations have already implemented data-driven supply chains, organizations are still faced with the challenges of static, siloed, and different functional supply chain applications; limited data exchange with key trading partners across upstream and downstream operations; and the inability to effectively leverage relevant external data.

At Google Cloud, we believe the key to meaningful and effective change is a data-driven supply chain that allows you to achieve visibility, flexibility, and innovation.

Our solutions help you prepare for the unpredictable and enhance the value of your data. By unlocking AI-driven insights, you can strengthen distribution networks and optimize your workflows and supply chains to become more reliable, intelligent, and sustainable. Some of the business challenges we address include:

• Predicting demand with Vertex AI Forecast
• Visual inspection for quality and predictive maintenance with pre-built ML models
• Automating and optimizing pickup and delivery operations with Cloud Fleet Routing API
• Real time and holistic inventory visibility with Supply Chain Twin

Make sure you’re prepared for the unpredictable with real-time visibility over your distribution networks. Learn how you can harness the power of AI and analytics and gain actionable insights that enhance your supply chain.

Today, a typical enterprise uses over 100 SaaS applications while large organizations commonly use over 400 apps. These applications contain valuable data on customers, suppliers, employees, products and more, offering the potential for valuable insights and powerful workflows. However, in the past, this data has often remained siloed and underutilized, even within an application.

Customer demand is driving an unprecedented wave of innovation across the SaaS industry, with providers building intelligence into their applications through rich analytical and AI/ML capabilities, and enabling their customers’ data ecosystems through real time data sharing. The data cloud platform powering these applications is a critical factor in companies’ ability to innovate and optimize efficiency, while keeping its customer’s data secure.

Today, over 700 tech companies including Zoominfo, Equifax, Exabeam, Bloomreach, and Quantum Metric power their products and businesses using Google’s data cloud. This week at the Data Cloud Summit, we announced the Built with BigQuery initiative which helps ISVs get started building applications using data and machine learning products. By providing dedicated access to technology, expertise and go to market programs, this initiative allows tech companies to accelerate, optimize and amplify their success.

“Enabling customers to gain superior insights and intelligence from data is core to the ZoomInfo strategy,” ZoomInfo CEO Henry Schuck says. “We are excited about the innovation Google Cloud is bringing to market and how it is creating a differentiated ecosystem that allows customers to gain insights from their data securely, at scale, and without having to move data around. Working with the Built with BigQuery initiative team enables us to rapidly gain deep insight into the opportunities available and accelerate our speed to market.”

Building intelligent SaaS applications with a unified data platform

Google’s data cloud provides a complete platform for building data-driven applications, from simplified data ingestion, processing and storage to powerful analytics, AI/ML and data sharing capabilities, all seamlessly integrated with Google Cloud’s open, secure, sustainable platform. With a huge partner ecosystem and support for multicloud, open source tools and APIs, we provide technology companies the portability and extensibility they need to avoid data lock-in.

Through the Built with BigQuery initiative, we are helping tech companies to build the next-gen SaaS applications on Google’s data cloud with simplified access to technology, dedicated engineering support and joint go to market programs.

Exabeam, a next generation cybersecurity solution, is a great example. The company leverages Google’s data cloud to provide their customers with a “limitless-scale” cybersecurity solution.

“Built with Google’s data cloud, Exabeam’s limitless-scale cybersecurity platform helps enterprises respond to security threats faster and more accurately” said Sanjay Chaudhary, VP of Products at Exabeam. “We are able to ingest data from over 500 security vendors, convert unstructured data into security events, and create a common platform to store them in a cost effective way. The scale and power of Google’s data cloud enables our customers to search multi-year data and detect threats in seconds.”

Foster innovation and unlock new business models through data sharing

Data becomes even more valuable when shared. According to research, the global data monetization market size is growing at a CAGR of 47.9% and is projected to reach $11.7 Billion by 2026. It’s a compelling proposition for SaaS companies as it enables them to deliver increased value for their customers while expanding their own partner ecosystem, increasing stickiness and unlocking new revenue streams.

Google Cloud’s strategy for data sharing encompasses three key areas: secure data sharing in BigQuery, the ability to create private and public exchanges alongside commercial and public datasets in Analytics Hub, and a robust ecosystem of premier partner and Google data.

Through the real-time data sharing capabilities of BigQuery, SaaS companies are enabling their customers to combine their data with the customer’s own proprietary data, and other third-party data sources, to derive 360-degree insights. Over 4,500 organizations share more than 250 Petabytes of data weekly in BigQuery, not accounting for intra-organizational data sharing*.

For example, manufacturers can get real-time visibility into their entire supply chain by combining datasets from ISVs, such as supply chain innovator, Blume Global, and data publishers.

“Our partnership with Google Cloud is helping us achieve our mission to build the next-generation supply chain operating system. Blume Maps, our digital twin of the supply chain world built with Google’s data cloud, allows our customers to generate accurate lead times, real-time shipment location and ETAs” said Blume Global CEO Pervinder Johar. “We apply the power of Google Cloud’s data and analytics capabilities to our growing database of over 1.5 million global data points to feed our lead time and dynamic ETA engine. We are also able to create data twins of unique logistics data and share with users around the globe.”

Google Cloud’s Analytics Hub is a fully-managed service built on BigQuery that allows organizations to efficiently and securely exchange valuable data and analytics assets across any organizational boundary. With unique datasets that are always-synchronized and bi-directional sharing, you can create a rich and trusted data ecosystem between business units or partnerships–one in which everyone gains value immediately.

“As external data becomes more critical to organizations across industries, the need for a unified experience between data integration and analytics has never been more important. We are proud to be working with Google Cloud to power the launch of Analytics Hub, feeding hundreds of pre-engineered data pipelines from hundreds of external datasets,” said Dan Lynn, SVP Product at Crux. “The sharing capabilities that Analytics Hub delivers will significantly enhance the data mobility requirements of practitioners, and the Crux data integration platform stands ready to quickly integrate any external data source and deliver on behalf of Google Cloud and its clients.”

Innovate, optimize and amplify with Google Cloud

The Built with BigQuery initiative provides access to technology, expertise and go-to-market:.

• Access to Technology: Get started fast with a Google-funded, pre-configured sandbox. Access Cloud Credits to fund your, and your customer’s, POCs and apply for innovative pricing models designed for ISVs.
• Access to Expertise: Accelerate and optimize product design and architecture with access to designated experts in building data-driven SaaS applications from the ISV Center of Excellence, providing insight into key use cases, architectural patterns and best practices. Access experts from across Google to enable co-innovation with products such as Earth Engine and Google Marketing Platform.
• Access to Go-to-market: Amplify your success with joint marketing programs to drive awareness, generate demand and increase adoption.

Get Started

Start building your applications on Google Cloud with $300 in free credits or apply for the Built with BigQuery initiative.

*As of November, 2021, within a typical 7 day period in BigQuery, and not accounting for intra-organizational data sharing.

At Precognitive, we were able to start with a blank technology slate to support our fraud detection software products. When we started building the initial version of our platform in 2017, we had some decisions to make: What coding language to use? What cloud infrastructure provider to choose? What database to use? The majority of the decisions were straightforward, but we struggled to decide upon a database. We had plenty of collective experience with relational databases, but not with a wide-column database like Cloud Bigtable—which we knew we’d need to scale our behavior and device workloads. At launch, our products were supported by a self-managed database, but we quickly migrated to Cloud Bigtable, and we love it.  

To efficiently support our bursty, real-time fraud detection workloads, we needed a cloud database that could satisfy the following key requirements:

• Stability to keep up with increased adoption of our products
• Intelligent scaling that avoids bottlenecks
• Native integrations with BigQuery and Cloud Dataproc
• Managed services that free up our engineers’ time to work on our products

Adding Cloud Bigtable as our performance database

As we scaled our services and added customers, our data collection services for our Device Intelligence and Behavioral Analytics products were seeing thousands of events per second. Cloud Bigtable provided a stable managed database that could handle the volume we were receiving during peak hours. We weren’t always able to handle this scale, as an early version of our product utilized a self-managed database.

Every month, two or three engineers spent hours managing the database instances. Whenever the instances crashed, it would cost at least one engineer a day or two of productivity attempting to restore the instances and recovering any data from our backup database. Managing this database internally was taking precious time away from product development.

We circled back to Cloud Bigtable. After two weeks of R&D, we decided to switch the Device Intelligence and Behavioral Analytics services to Cloud Bigtable.

Cloud Bigtable solved our scaling issues. Cloud Bigtable had been attractive to us from the start because it was fully managed, and offered regional replication and other features we were lacking in our own managed instances. Cloud Bigtable provides horizontal scaling and automatically rebalances row keys (equivalent to a shard key) over time to prevent “hot” nodes. In addition, Cloud Bigtable provides a connector to BigQuery and Cloud Dataproc that allows us to analyze the terabytes of data we are processing and use that data for unsupervised machine learning.

The perks of using Cloud Bigtable

After the migration to Cloud Bigtable, we noticed a number of additional benefits: improved I/O performance, a significant cost reduction, and a sizable decrease in hours spent on database maintenance.

We measured some of our typical metrics before and after implementing Cloud Bigtable. Our request latency dropped by about 30 ms on average (to sub-10 ms) for API requests. Prior to the change, we were seeing latencies of 40+ ms on average. This latency drop on our Behavioral Analytics and Device Intelligence products allowed us to trim about an additional 10 to 15 ms off our average response time across all dependent services.

Before we moved to Cloud Bigtable, we had to scale our database instances every time a new customer was onboarded. We were over-scaling in an attempt to avoid constantly resizing our database servers. By sunsetting our self-managed database and switching to Cloud Bigtable, we cut database infrastructure costs by approximately 35% and can now scale as needed, with a couple of clicks, during onboarding.

We have spent zero hours managing a Cloud Bigtable database since launch, and we put the time we are saving every month toward product development.

Moving forward with Cloud Bigtable

As an engineering team, we love working with Cloud Bigtable. We are not only seeing improved developer experience and reduced latency, which keeps the engineers happy, but also reduced costs, which keeps the business happy. We’re able to build more product, too, with the time we’ve saved by switching to Cloud Bigtable. Stay tuned to our engineering blog for more on the lessons we’ve learned and our contributions to the wider Cloud Bigtable community.